Identification and quantitative prediction of large and superlarge mineral deposits of solid mineral resources using the mineral resource prediction theory and method with comprehensive information is carried out nati...Identification and quantitative prediction of large and superlarge mineral deposits of solid mineral resources using the mineral resource prediction theory and method with comprehensive information is carried out nationwide in China at a scale of 1∶5 000 000. Using deposit concentrated regions as the model units and concentrated mineralization anomaly regions as prediction units, the prediction is performed on GIS platform. The technical route and research method of locating large and superlarge mineral deposits and principle of compiling attribute table of independent variables and functional variables are proposed. Upon methodology study, the qualitative locating and quantitative predicting mineral deposits are carried out with quantitative theory Ⅲ and characteristic analysis, respectively, and the advantage and disadvantage of two methods are discussed. This research is significant for mineral resource prediction in ten provinces of western China.展开更多
As the applications for modeling of big data and analysis advance in scope,computational efficiency faces greater challenges in terms of storage and speed.In many practical problems,a great amount of historical data i...As the applications for modeling of big data and analysis advance in scope,computational efficiency faces greater challenges in terms of storage and speed.In many practical problems,a great amount of historical data is sequentially collected and used for online statistical modeling.For modeling sequential data,we propose a sequential linear regression method that extracts essential information from historical data.This carefully selected information is then utilized to update a model according to a sequential estimation scheme.With this technique,the earlier data no longer needs to be stored,and the sequential updating is computationally efficient in speed and storage.A weighted strategy is introduced on the current model to determine the impact of data from different periods.When compared with estimation methods that use historical data,our numerical experiments demonstrate that our solution increases the speed while decreasing the storage load.展开更多
文摘Identification and quantitative prediction of large and superlarge mineral deposits of solid mineral resources using the mineral resource prediction theory and method with comprehensive information is carried out nationwide in China at a scale of 1∶5 000 000. Using deposit concentrated regions as the model units and concentrated mineralization anomaly regions as prediction units, the prediction is performed on GIS platform. The technical route and research method of locating large and superlarge mineral deposits and principle of compiling attribute table of independent variables and functional variables are proposed. Upon methodology study, the qualitative locating and quantitative predicting mineral deposits are carried out with quantitative theory Ⅲ and characteristic analysis, respectively, and the advantage and disadvantage of two methods are discussed. This research is significant for mineral resource prediction in ten provinces of western China.
基金supported by the National Natural Science Foundation of China(Nos.11171322,11426236)the Fundamental Research Funds for the Central Universities(WK0010000051).
文摘As the applications for modeling of big data and analysis advance in scope,computational efficiency faces greater challenges in terms of storage and speed.In many practical problems,a great amount of historical data is sequentially collected and used for online statistical modeling.For modeling sequential data,we propose a sequential linear regression method that extracts essential information from historical data.This carefully selected information is then utilized to update a model according to a sequential estimation scheme.With this technique,the earlier data no longer needs to be stored,and the sequential updating is computationally efficient in speed and storage.A weighted strategy is introduced on the current model to determine the impact of data from different periods.When compared with estimation methods that use historical data,our numerical experiments demonstrate that our solution increases the speed while decreasing the storage load.
